Difference between revisions of "2016 Winter Project Week/Projects/BatchImageAnalysis"

From NAMIC Wiki
Jump to: navigation, search
 
(14 intermediate revisions by 4 users not shown)
Line 1: Line 1:
 
__NOTOC__
 
__NOTOC__
 
<gallery>
 
<gallery>
Image:PW-MIT2016.png|[[2016_Winter_Project_Week#Projects|Projects List]]
+
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
 +
<!-- Use the "Upload file" link on the left and then add a line to this list like "File:MyAlgorithmScreenshot.png" -->
 
</gallery>
 
</gallery>
 +
 +
[[File:LungCT-3DSIFT.png|200px|thumb|left|3D SIFT Lung Features]]
  
 
==Key Investigators==
 
==Key Investigators==
Kalli Retzepi
+
<!-- Add a bulleted list of investigators and their institutions here -->
Yangming Ou
+
 
Matt Toews
+
* Kalli Retzepi (MGH)
Steve Pieper
+
* Yangming Ou (MGH)
Sandy Wells
+
* Matt Toews (ETS)
Randy Gollub
+
* Lilla Zollei (MGH)
 +
* Lauren O'Donnell (BWH)
 +
* Steve Pieper (BWH)
 +
* Sandy Wells (BWH)
 +
* Randy Gollub (MGH)
  
 
==Project Description==
 
==Project Description==
<div style="margin: 20px;">
+
{| class="wikitable"
<div style="width: 27%; float: left; padding-right: 3%;">
+
! style="text-align: left; width:27%" |  Objective
<h3>Objective</h3>
+
! style="text-align: left; width:27%" |  Approach and Plan
*
+
! style="text-align: left; width:27%" |  Progress and Next Steps
</div>
+
|- style="vertical-align:top;"
<div style="width: 27%; float: left; padding-right: 3%;">
+
|
<h3>Approach, Plan</h3>
+
<!-- Objective bullet points -->
*
+
* Run feature detection code over a collection of medical images pulled from PACS
</div>
+
* Investigate a collection of ADC maps of neonates (diffusion MR)
<div style="width: 27%; float: left; padding-right: 3%;">
+
* Patients labeled with age and health status (normal, mildly abnormal, severely abnormal)
<h3>Progress</h3>
+
* Use 3D SIFT code to see if health status can be detected in images
*
+
* (if time) try text analysis of radiology reports
</div>
+
|
</div>
+
<!-- Add a bulleted list of key points -->
 +
* Use deidentified cohort of neonate images collected from MGH
 +
* Install data and software on AWS, try StarCluster
 +
* Explore visualization options
 +
* (if time) integrate image features with analysis of radiology report text
 +
|
 +
<!-- Fill this out at the end of Project Week; describe what you did this week and what you plan to do next -->
 +
 
 +
Algorithm
 +
 
 +
* feature extraction (20 seconds per image)
 +
 
 +
* Feature matching O(log N) indexing (< 1 second per image)
 +
 
 +
* 3D SIFT-Rank code (Windows, Linux, Max)  and read me
 +
http://www.matthewtoews.com/fba/featExtract1.5.zip
 +
 
 +
Result
 +
Baseline HIE classification rate: 73%, leave-one-out moderate vs normal.
 +
 
 +
 
 +
Data
 +
231 subjects, Apparent Diffusion Coefficient (ADC) images.
 +
 
 +
|}
 +
 
 +
==Features Extracted in ADC MRI Volume ==
 +
 
 +
http://wiki.na-mic.org/Wiki/images/b/b2/Image_%282%29.png

Latest revision as of 15:54, 8 January 2016

Home < 2016 Winter Project Week < Projects < BatchImageAnalysis
3D SIFT Lung Features

Key Investigators

  • Kalli Retzepi (MGH)
  • Yangming Ou (MGH)
  • Matt Toews (ETS)
  • Lilla Zollei (MGH)
  • Lauren O'Donnell (BWH)
  • Steve Pieper (BWH)
  • Sandy Wells (BWH)
  • Randy Gollub (MGH)

Project Description

Objective Approach and Plan Progress and Next Steps
  • Run feature detection code over a collection of medical images pulled from PACS
  • Investigate a collection of ADC maps of neonates (diffusion MR)
  • Patients labeled with age and health status (normal, mildly abnormal, severely abnormal)
  • Use 3D SIFT code to see if health status can be detected in images
  • (if time) try text analysis of radiology reports
  • Use deidentified cohort of neonate images collected from MGH
  • Install data and software on AWS, try StarCluster
  • Explore visualization options
  • (if time) integrate image features with analysis of radiology report text

Algorithm

  • feature extraction (20 seconds per image)
  • Feature matching O(log N) indexing (< 1 second per image)
  • 3D SIFT-Rank code (Windows, Linux, Max) and read me

http://www.matthewtoews.com/fba/featExtract1.5.zip

Result Baseline HIE classification rate: 73%, leave-one-out moderate vs normal.


Data 231 subjects, Apparent Diffusion Coefficient (ADC) images.

Features Extracted in ADC MRI Volume

Image_%282%29.png